Tutorials

Welcome to the LangGraph Tutorials! These notebooks introduce LangGraph through building various language agents and applications.

Quick Start

Learn the basics of LangGraph through a comprehensive quick start in which you will build an agent from scratch.

• Quick Start

Use cases

Learn from example implementations of graphs designed for specific scenarios and that implement common design patterns.

Chatbots

• Customer Support: Build a customer support chatbot to manage flights, hotel reservations, car rentals, and other tasks
• Prompt Generation from User Requirements: Build an information gathering chatbot
• Code Assistant: Build a code analysis and generation assistant

Multi-Agent Systems

• Collaboration: Enable two agents to collaborate on a task
• Supervision: Use an LLM to orchestrate and delegate to individual agents
• Hierarchical Teams: Orchestrate nested teams of agents to solve problems

RAG

• Adaptive RAG
  • Adaptive RAG using local LLMs
• Agentic RAG
• Corrective RAG
  • Corrective RAG using local LLMs
• Self-RAG
  • Self-RAG using local LLMs
• SQL Agent

Planning Agents

• Plan-and-Execute: Implement a basic planning and execution agent
• Reasoning without Observation: Reduce re-planning by saving observations as variables
• LLMCompiler: Stream and eagerly execute a DAG of tasks from a planner

Reflection & Critique

• Basic Reflection: Prompt the agent to reflect on and revise its outputs
• Reflexion: Critique missing and superfluous details to guide next steps
• Language Agent Tree Search: Use reflection and rewards to drive a tree search over agents
• Self-Discover Agent: Analyze an agent that learns about its own capabilities

Evaluation

• Agent-based: Evaluate chatbots via simulated user interactions
• In LangSmith: Evaluate chatbots in LangSmith over a dialog dataset

Experimental

• Web Research (STORM): Generate Wikipedia-like articles via research and multi-perspective QA
• TNT-LLM: Build rich, interpretable taxonomies of user intentand using the classification system developed by Microsoft for their Bing Copilot application.
• Web Navigation: Build an agent that can navigate and interact with websites
• Competitive Programming: Build an agent with few-shot "episodic memory" and human-in-the-loop collaboration to solve problems from the USA Computing Olympiad; adapted from the "Can Language Models Solve Olympiad Programming?" paper by Shi, Tang, Narasimhan, and Yao.
• Complex data extraction: Build an agent that can use function calling to do complex extraction tasks

Comments